attr): attr_val = _orig_module_getattr(mod, attr) return self.getattr(attr, attr_val, parameter_proxy_cache)# 定义对于`nn.Module`的forward方法的包装@functools.wraps(_orig_module_call)def module_call_wrapper(mod, *args, **kwarg...
defforward(ctx,input): """ In the forward pass we receive a Tensor containing the input and return a Tensor containing the output. ctx is a context object that can be used to stash information for backward computation. You can cache arbitrary objects for use in the backward pass using the...
6、使用torch.cuda.empty_cache()清除 GPU 缓存，如果你想在使用 notebook 时删除并重新创建一个大模...
verbose_log=False,timing_cache_prefix="",save_timing_cache=False,cuda_graph_batch_size=-1,dynami...
In the forward pass we receive a Tensor containing the input and return a Tensor containing the output. ctx is a context object that can be used to stash information for backward computation. You can cache arbitrary objects for use in the backward pass using the ctx.save_for_backward method...
return self.getattr(attr, attr_val, parameter_proxy_cache) # 定义对于`nn.Module`的forward方法的包装 @functools.wraps(_orig_module_call) def module_call_wrapper(mod, *args, **kwargs): def forward(*args, **kwargs): return _orig_module_call(mod, *args, **kwargs) ...
_C._jit_script_compile( qualified_name, ast, _rcb, get_default_args(obj) ) # Forward docstrings fn.__doc__ = obj.__doc__ # cache起来 _set_jit_function_cache(obj, fn) return fn 我们看下get_jit_def是如何得到 jit 规定的 ast 语法树的 仅保留逻辑代码,细节删掉 代码语言:javascript ...
Mega Cache PyTorch Native Context Parallel Enhancing Intel GPU Acceleration FlexAttention LLM first token processing on X86 CPUs FlexAttention LLM throughput mode optimization on X86 CPUs Foreach Map Flex Attention for Inference Prologue Fusion Support in Inductor For more details about these ...
importtorchclassTinyModel(torch.nn.Module):def__init__(self):super(TinyModel,self).__init__()self.linear1=torch.nn.Linear(100,200)self.activation=torch.nn.ReLU()self.linear2=torch.nn.Linear(200,10)self.softmax=torch.nn.Softmax()defforward(self,x):x=self.linear1(x)x=self.activation...
(ctx, input):"""In the forward pass we receive a Tensor containing the input and returna Tensor containing the output. ctx is a context object that can be usedto stash information for backward computation. You can cache arbitraryobjects for use in the backward pass using the ctx.save_for...